An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Multispectral Skin Color Modelling

The automated detection of humans in computer vision as well as the realistic rendering of people in computer graphics necessitates improved modeling of the human skin color. We describe the acquisition and modeling of skin reflectance data densely sampled over the entire visible spectrum. The data collected through a spectrograph allows us to explain skin color (and its variations) and to discriminate between human skin and dyes designed to mimic human skin. We study the approximation of these data using several sets of basis functions. Our study shows that skin reflectance data can best be approximated by a linear combination of Gaussians or their first derivatives. This result has a significant practical impact on optical acquisition devices: the entire visible spectrum of skin reflectance can now be captured with a few filters of optimally chosen central wavelengths and bandwidth

Laser Scanner Technology

This paper addresses the basic principles, performance measures and applications associated with laser scanner technologies. The objective of this report is to communicate and disseminate pertinent information related to state-of-the-art laser measurement systems that are currently available through commercial and research means. This paper should serve two-fold: (1) as a basic tutorial to laser scanning technology and (2) as a guide to current manufacturers and researchers of this technology

Illuminant Estimation by Voting

Obtaining an estimate of the illuminant color is an important component in many image analysis applications. Due to the complexity of the problem many restrictive assumptions are commonly applied, making the existing illuminant estimation methodologies not widely applicable on natural images. We propose a methodology which analyzes a large number of regions in an image. An illuminant estimate is obtained independently from each region and a global illumination color is computed by consensus. Each region itself is mainly composed by pixels which simultaneously exhibit both diffuse and specular reflection. This allows for a larger inclusion of pixels than purely specularitybased methods, while avoiding, at the same time, some of the restrictive assumptions of purely diffuse-based approaches. As such, our technique is particularly well-suited for analyzing real-world images. Experiments with laboratory data show that our methodology outperforms 75 % of other illuminant estimation methods. On natural images, the algorithm is very stable and provides qualitatively correct estimates. 1

Spectral Gradient: A Surface Reflectance Measurement Invariant to Geometry and Incident Illumination

Although photometric data is a readily available dense source of information in intensity images, it is not widely used in computer vision. A major drawback is its dependence on viewpoint and incident illumination. A novel methodology is presented which extracts reflectivity information of the various materials in the scene independent of incident light and scene geometry. A scene is captured under three different narrow-band color filters and the spectral derivatives of the scene are computed. The resulting spectral derivatives form a spectral gradient at each pixel. This spectral gradient is a surface reflectance descriptor which is invariant to scene geometry and incident illumination for smooth diffuse surfaces. The invariant properties of the spectral gradients make them a particularly appealing tool in many diverse areas of computer vision such as color constancy, tracking, scene classification, material classification, stereo correspondence, even re-illumination of a scene

Edge detection in multispectral images using the n-dimensional self-organizing map

We propose a new method for performing edge detection in multi-spectral images based on the self-organizing map (SOM) concept. Previously, 1-dimensional or 2-dimensional SOMs were trained to provide a linear mapping of high-dimensional multispectral vectors. Then, edge detection was applied on that mapping. However, the 1-dimensional SOM may not converge on a suitable global order for images with rich content. Likewise, the 2-dimensional SOM intro-duces false edges due to linearization artifacts. Our method feeds the edge detector without linearization. Instead, it exploits directly the distances of SOM neurons. This avoids the aforementioned draw-backs and is more general, as a SOM of arbitrary dimensionality can be used. We show that our method achieves significantly bet-ter edge detection results than previous work on a high-resolution multispectral image database. Index Terms — Multispectral imaging, Image edge detection, Self organizing feature maps, Machine Visio

Chromoendoscopy in magnetically guided capsule endoscopy

BACKGROUND: Diagnosis of intestinal metaplasia and dysplasia via conventional endoscopy is characterized by low interobserver agreement and poor correlation with histopathologic findings. Chromoendoscopy significantly enhances the visibility of mucosa irregularities, like metaplasia and dysplasia mucosa. Magnetically guided capsule endoscopy (MGCE) offers an alternative technology for upper GI examination. We expect the difficulties of diagnosis of neoplasm in conventional endoscopy to transfer to MGCE. Thus, we aim to chart a path for the application of chromoendoscopy on MGCE via an ex-vivo animal study. METHODS: We propose a modified preparation protocol which adds a staining step to the existing MGCE preparation protocol. An optimal staining concentration is quantitatively determined for different stain types and pathologies. To that end 190 pig stomach tissue samples with and without lesion imitations were stained with different dye concentrations. Quantitative visual criteria are introduced to measure the quality of the staining with respect to mucosa and lesion visibility. Thusly determined optimal concentrations are tested in an ex-vivo pig stomach experiment under magnetic guidance of an endoscopic capsule with the modified protocol. RESULTS: We found that the proposed protocol modification does not impact the visibility in the stomach or steerability of the endoscopy capsule. An average optimal staining concentration for the proposed protocol was found at 0.4% for Methylene blue and Indigo carmine. The lesion visibility is improved using the previously obtained optimal dye concentration. CONCLUSIONS: We conclude that chromoendoscopy may be applied in MGCE and improves mucosa and lesion visibility. Systematic evaluation provides important information on appropriate staining concentration. However, further animal and human in-vivo studies are necessary

The Color of Specular Highlights

An integral part of computer graphics, machine vision and human vision understanding is modeling how a surface reflects light. There is a substantial body of work on models describing surface reflectance ranging from purely matte to purely specular. One of the advantages of diffuse reflectance is that the color and the intensity of the reflected light are separable for most materials. Color is determined by the chromophores of the material, while intensity depends on the scene geometry. In specular highlights the color and the intensity of a specularity depend on both the geometry and the index of refraction of the material, which in turn is a function of wavelength. The graphics and vision communities often employ the following simplifying assumption when modeling specular highlights: For non-conductive materials the color of the specularity is the color of the light source. We will show that in most cases this assumption is violated. Theoretical analysis demonstrates that even for non-metallic surfaces the reflectivity ratio at specularities varies with both wavelength and angle of incidence. Furthermore, our experiments with a multispectral sensor clearly show that the deviation of the color of the specularities from the color of the incident light can be consistently measured